High-throughput combination screening of Pidnarulex and other G-quadruplex ligands in multi-cell type tumor spheroids.

G-quadruplexes (G4s) are four-stranded nucleic acid structures that regulate key cellular processes and represent promising therapeutic targets in oncology. To investigate the therapeutic potential of three G4 ligands-pidnarulex, APTO-253, and BRACO-19-a high-throughput drug combination screen was conducted in thirty-one multi-cell type tumor spheroids derived from patient tumors and established cancer cell lines. These 3D spheroids mimic key features of the tumor microenvironment, comprising malignant, endothelial, and mesenchymal cell populations. Compounds selected for combination screening included agents with mechanistic relevance to G4 biology, such as inhibitors of DNA damage response (DDR), replication stress, and chromatin regulation, based on the proposed roles of G4s in replication and genome stability. Combination responses were assessed using cell viability assays and supported by longitudinal brightfield imaging to monitor spheroid morphology and growth dynamics. Drug interactions were quantified using Bliss independence scores and the volume under the viability surface, providing complementary metrics of synergy and overall response. Among the G4 ligands, pidnarulex demonstrated the broadest single-agent activity, while APTO-253 and BRACO-19 showed limited effects. Model-specific synergy was observed from combinations with inhibitors of PARP, DDR kinases (ATM, ATR, DNA-PK), and cell cycle regulators (WEE1, PIM1). Interestingly, pidnarulex exhibited consistent synergy in one of eight pancreatic adenocarcinoma models (966289-007-R4-J1) across multiple DDR-targeted combinations. Combination interactions were also observed with HDAC inhibitors in a subset of models. Brightfield imaging corroborated enhanced spheroid growth suppression from synergistic combinations. These findings underscore the context-dependent activity of G4 ligands and support the use of integrated functional and imaging-based approaches to characterize potential therapeutic combinations in physiologically relevant 3D cancer models.